Match-LSTM | PyTorch implemention of Match-LSTM , R-NET | Natural Language Processing library

by laddie132 Python Version: Current License: MIT

X-Ray Key Features Code Snippets Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | Match-LSTM Summary

Match-LSTM is a Python library typically used in Artificial Intelligence, Natural Language Processing, Deep Learning, Pytorch applications. Match-LSTM has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. However Match-LSTM build file is not available. You can download it from GitHub.

A PyTorch implemention of Match-LSTM, R-NET and M-Reader for Machine Reading Comprehension

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Match-LSTM has a low active ecosystem.

It has 95 star(s) with 19 fork(s). There are 3 watchers for this library.

It had no major release in the last 6 months.

There are 1 open issues and 4 have been closed. On average issues are closed in 13 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of Match-LSTM is current.

Quality

Match-LSTM has 0 bugs and 0 code smells.

Security

Match-LSTM has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

Match-LSTM code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

Match-LSTM is licensed under the MIT License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

Match-LSTM releases are not available. You will need to build from source code and install.

Match-LSTM has no build file. You will be need to create the build yourself to build the component from source.

Installation instructions are not available. Examples and code snippets are available.

Match-LSTM saves you 1325 person hours of effort in developing the same functionality from scratch.

It has 2972 lines of code, 181 functions and 30 files.

It has medium code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed Match-LSTM and discovered the below as its top functions. This is intended to give you an instant insight into Match-LSTM implemented functionality, and help decide if they suit your requirements.

Create a trained model
Evaluate E
Evaluate the model on the given model
Evaluate F1 score
Collect function data from batch
Convert a batch of words to a single word
Removes all zeros from the right
Forward embedding
Performs an answer search
Draw a heatmap
Count the number of parameters in the model
Draw the score for each epoch
Transform Quad_glove dataset to hdf5 file
Draw training and eval loss
Evaluate the prediction
Compute the gradient for the given h
Export to csv file
Parse an analysis log file
Generate batch context
Gather the length of the answer sequence
Calculates the length of the analysis
Convert a feature dict into a token vector
Gather the length of a context sequence
Extract the analysis type of a question type
Evaluate predictions with wrong match
Compare two texts

Get all kandi verified functions for this library.

Match-LSTM Key Features

No Key Features are available at this moment for Match-LSTM.

Match-LSTM Examples and Code Snippets

No Code Snippets are available at this moment for Match-LSTM.

Community Discussions

Trending Discussions on Match-LSTM

`for` loop to a multi dimensional array in PyTorch

QUESTION

`for` loop to a multi dimensional array in PyTorch

Asked 2017-Nov-23 at 17:26

I want to implement Q&A systems with attention mechanism. I have two inputs; context and query which shapes are (batch_size, context_seq_len, embd_size) and (batch_size, query_seq_len, embd_size).
I am following the below paper. Machine Comprehension Using Match-LSTM and Answer Pointer. https://arxiv.org/abs/1608.07905

Then, I want to obtain a attention matrix which shape is (batch_size, context_seq_len, query_seq_len, embd_size). In the thesis, they calculate values for each row (it means each context word, G_i, alpha_i in the paper).

My code is below and it is running. But I am not sure my way is good or not. For example, I use for loop for generating sequence data (for i in range(T):). And to obtain each row, I use in-place operator like G[:,i,:,:], embd_context[:,i,:].clone() is a good manner in pytorch? If not, where should I change the code?

And if you notice other points, let me know. I am a new in this field and pytorch. Sorry for my ambiguous question.

...

ANSWER

Answered 2017-Nov-23 at 17:26

I think your code is fine. You can't avoid the loop: for i in range(T): because in equation (2) in the paper (https://openreview.net/pdf?id=B1-q5Pqxl), there is a hidden state coming from Match-LSTM cell which is involved in computing G_i and alpha_i vector and they are used to compute the input for next timestep of the Match-LSTM. So, you need to run the loop for every timestep of the Match-LSTM, I don't see an alternative to avoid the for loop anyway.

Source https://stackoverflow.com/questions/47417159

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install Match-LSTM

You can download it from GitHub.
You can use Match-LSTM like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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